Design of Resilient Supply Chains with Risk of Facility Disruptions

<p>. The design of resilient supply chains under the risk of disruptions at candidate locations for distribution centers (DCs) is formulated as a two-stage stochastic program. The problem involves selecting DC locations, determining storage capacities for multiple commodities, and establishing the distribution strategy in scenarios that describe disruptions at potential DCs. The objective is to minimize the sum of investment cost and expected distribution cost during a finite time-horizon. The rapid growth in the number of scenarios requires the development of an effective method to solve large-scale problems. The method includes a strengthened multi-cut Benders decomposition algorithm and the derivation of deterministic bounds based on the optimal solution over reduced sets of scenarios. Resilient designs for a large-scale example and an industrial supply chain are found with the proposed method. The results demonstrate the importance of including DC capacity in the design problem and anticipating the distribution strategy in adverse scenarios.</p

Real Option Management of Hydrocarbon Cracking Operations

Commodity conversion assets play important economic roles. It is well known that the market value of these assets can be maximized by managing them as real options on the prices of their inputs and/or outputs. In particular, when futures on these inputs and outputs are traded, managing such real options, that is, valuing, hedging, and exercising them, is analogous to managing options on such futures, using risk neutral valuation and delta hedging methods. This statement holds because dynamically trading portfolios of these futures and a risk less bond can replicate the cash °ows of these assets. This basic principle is not always appreciated by managers of commodity conversion assets. Moreover, determining the optimal operational cash °ows of such an asset requires optimizing the asset operating policy. This issue complicates the real option management of commodity conversion assets. This chapter illustrates the application of this approach to manage a hydrocarbon cracker, a specific commodity conversion asset, using linear programming and Monte Carlo simulation. The discussion is based on a simplified representation of the operations of this asset. However, the material presented here has potential applicability to the real option management of more realistic models of hydrocarbon cracking assets, as well as other energy and commodity conversion assets.</p

Data-driven multi-stage scenario tree generation via statistical property and distribution matching

<p>This paper brings systematic methods for scenario tree generation to the attention of the Process Systems Engineering community. We focus on a general, data-driven optimization-based method for generating scenario trees that does not require strict assumptions on the probability distributions of the uncertain parameters. Using as a basis the Moment Matching Problem (MMP), originally proposed by Høyland and Wallace (2001), we propose matching marginal (Empirical) Cumulative Distribution Function information of the uncertain parameters in order to cope with potentially under-specified MMP formulations. The new method gives rise to a Distribution Matching Problem (DMP) that is aided by predictive analytics. We present two approaches for generating multi-stage scenario trees by considering time series modeling and forecasting. The aforementioned techniques are illustrated with a production planning problem with uncertainty in production yield and correlated product demands.</p

Hybrid Bilevel-Lagrangean Decomposition Scheme for the Integration of Planning and Scheduling of a Network of Batch Plants

<p>Motivated by a real-world industrial problem, this work deals with the integration of planning and scheduling in the operation of a network of batch plants. The network consists of single-stage, multiproduct batch plants located in different sites, which can exchange intermediate products in order to blend them to obtain finished products. The time horizon is given and divided into multiple time periods, at the end of which, the customer demands have to be exactly satisfied. The planning model is a simplified and aggregate formulation derived from the detailed precedence-based scheduling formulation. Traveling Salesman Problem (TSP) constraints are incorporated at the planning level in order to predict the sequence-dependent changeovers between groups of products, within and across time periods, without requiring the detailed timing of operations, which is performed at the scheduling level. In an effort to avoid solving the full-space, rigorous scheduling model, especially for large problem sizes, two decomposition strategies are investigated: Bilevel and Temporal Lagrangean. We demonstrate that Bilevel Decomposition is efficient for small to medium problem instances and that further decomposition of the planning problem, yielding a hybrid decomposition scheme, is advantageous for tackling a large-scale industrial test case.</p

Optimal design of reliable integrated chemical production sites

Since plants that form the process network are subjected to fluctuations in product demand or random mechanical failures, design decisions such as adding redundant units and increasing storage between units can increase the flexibility and reliability of an integrated site. In this paper, we develop a bi-criterion optimization model that captures the trade-off between capital investment and process robustness in the design of an integrated site. Design decisions considered are increases in process capacity, introduction of parallel units, and addition of intermediate storage. The mixed-integer linear programming (MILP) formulation proposed in this paper includes the representation of the material levels in the intermediate storage by means of a probabilistic model that captures the effects of the discrete, uncertain events. We also integrate a superstructure optimization with stochastic modeling techniques such as continuous-time Markov chains. The application of the proposed model is illustrated with two example problems.</p

An efficient method for optimal design of large-scale integrated chemical production sites with endogenous uncertainty

Integrated sites are tightly interconnected networks of large-scale chemical processes. Given the large-scale network structure of these sites, disruptions in any of its nodes, or individual chemical processes, can propagate and disrupt the operation of the whole network. Random process failures that reduce or shut down production capacity are among the most common disruptions. The impact of such disruptive events can be mitigated by adding parallel units and/or intermediate storage. In this paper, we address the design of large-scale, integrated sites considering random process failures. In a previous work (Terrazas-Moreno et al., 2010), we proposed a novel mixed integer linear programming (MILP) model to maximize the average production capacity of an integrated site while minimizing the required capital investment. The present work deals with the solution of large-scale problem instances for which a strategy is proposed that consists of two elements. On one hand, we use Benders decomposition to overcome the combinatorial complexity of the MILP model. On the other hand, we exploit discrete-rate simulation tools to obtain a relevant reduced sample of failure scenarios or states. We first illustrate this strategy in a small example. Next, we address an industrial case study where we use a detailed simulation model to assess the quality of the design obtained from the MILP model.</p